Announcement: Revised Edition of the Technical Note Published on Zenodo A revised edition of the technical note “A Construction Method for Neural Networks Without Activation Functions” has been published on Zenodo. This updated version includes a newly added Appendix, which provides a comprehensive guide to a series of articles documenting practical implementations of the Tenda Categorization Network (TCN). These resources illustrate TCN’s applications in supervised learning, unsupervised learning, reinforcement learning, anomaly detection, hierarchical decision systems, and more. Importantly, the Appendix highlights how TCN functions as a language reasoning engine, offering a potential solution to the structural limitations inherent in current Large Language Models (LLMs). It is hoped that this revised edition will support deeper understanding and further research on TCN as a promising architecture for safe, interpretable, and structurally grounded AI.
Abstract: 5G is rolled out and next generation 6G networks are also being developed, ultra-low latency (URLL) communication as a standard is critical in supporting the plethora of applications, spanning autonomous vehicles, immersive extended reality experience, etc. However, tra…
We present a comprehensive automated solution for 3D seismic fault detection and interpretation that combines deep learning with advanced geometric post-processing. The method integrates a 3D U-Net neural network trained on synthetic data with normalized distance function targets…
Julia implementation of a scaled projection neural network for quasi-variational inequalities with state-dependent constraint set S(x) = m(x) + S and fixed symmetric positive-definite matrix M. Integrates the continuous-time dynamics dx/dt = lambda * [P_{S(x),M^{-1}}(x - alpha *…
The increasing reliance on third-party packages from repositories such as Python Package Index (PyPI) and Node Package Manager (NPM) has introduced critical vulnerabilities in software supply chains. Traditional security approaches, including signature-based detection and trust e…
We present a Graph Neural Network (GNN) framework for the classification of finite groups according to their solvability. Using undirected Cayley graph representations, the proposed framework learns to distinguish solvable and non-solvable groups directly from structural graph in…